A Baby Robot Platform for Cognitive Developmental Robotics
Takashi Minato∗ , Fabio DallaLibera† , Satoshi Yokokawa‡,
Yutaka Nakamura§, Hiroshi Ishiguro∗‡ , Emanuele Menegatti†∗ Asada Project, ERATO, Japan Science and Technology Agency,

Osaka University, 2-1 Yamada-oka, Suita, Osaka, 565-0871, Japan† Department of Information Engineering (DEI), Faculty of Engineering,

University of Padua, Via Gradenigo 6/a, I-35131 Padova, Italy‡ Departmemt of Systems Innovation, Graduate School of Engineering Science,

Osaka University, 2-1 Yamada-oka, Suita, Osaka, 565-0871, Japan§ Department of Adaptive Machine Systems, Graduate School of Engineering,Osaka University, 2-1 Yamada-oka, Suita, Osaka, 565-0871, Japan

Abstract— This paper presents a new baby robot platform
for cognitive developmental robotics developed by the SociallySynergistic Intelligence group of JST ERATO Asada Project.
We aim at developing a baby-sized humanoid robot with highperformance mobility to tackle research issues which have notbeen covered by robots developed in our group, for example,
motor learning through whole-body motor babbling and motion
generation with tactile interaction. For this purpose, powerfuland easily controllable actuators as well as a robust structureable to absorb shocks are chosen to allow the development ofdynamical motions. Furthermore the robot surface is covered
by touch sensors in order to allow studies in physical humanrobot interaction. Two research topics that will employ the newplatform are presented. The former involves the exploitationof biological fluctuations, a control method inspired from thechemotaxis of bacteria and from gene expressions of growing
cells, to learn a crawling motion. The second aims at studying
touch as a communication mean to develop robot motions.

I. I NTRODUCTION
Cognitive developmental robotics (CDR) [1] has been
focused on as a new approach to study mechanisms ofhuman intelligence, cognition, and development with robots
and computer simulations. In this approach we usually hypothesize a model of infant development based on existingknowledge in psychology and brain science, implement themodel in a humanoid robot which has humanlike kinematics
and a perceptual system similar to humans, and verify
the hypothesis by comparing phenomena observed in the
resultant robot behaviors with the actual infant development.
This requires a humanoid robot which can behave in ababy-like manner and naturally interact with persons, and,
therefore, a development of humanoid robot platform is also
important. In order to cover a wider area of research issues
on infant development, it is required that the robot platform
has a baby-sized body, a multimodal perception system, anda motor system to enable dynamic whole-body movements.
Another important factor in the development of a platform ismaintainability, which is necessary for sharing the platform
among researchers who work in CDR or have interest in
CDR. The growing interest in CDR and the consequent
need of new baby robot platforms seems to be confirmedby the recent development of Child robots like CB2 , Childrobot with Biomimetic Body [2], and iCub [3]. CB2 is ahumanoid robot driven by 56 actuators most of which are
pneumatic to allow a safe interaction with humans. This robot
is equipped with encoders on all the joints, accelerometersand gyroscopes on the three axis, two cameras located in
the eyeballs, two microphones mounted in the head andwith PVDF (Polyvinylidene Difluoride [4]) film bases tactile
sensors distributed over the whole body. Similarly iCubpresents a high number of degrees of freedom, precisely
53, which are however powered by electrical motors. This
platform is also equipped with encoders, a binocular visionsystem, gyroscopes and accelerometers, microphones andforce/torque sensors. Sensor skin based on a mesh of sensors
interconnected is currently under development [5]. Both ofthese two robots are quite big and heavy, expressly CB2 isabout 130cm high and weights 33Kg while iCub is 94cm talland weights 22Kg.
In order to decrease the cost, to increase the user safety bydiminishing the necessary motor torques and to allow closeinteractions like hugging we developed a new baby robot
platform of reduced size, whose picture is reported in Fig 1.
The developed platform is a completely autonomous,
battery powered and servomotor actuated baby robot. The
robot weights about 3Kg and is approximately 50 cm tall.
As its bigger counterparts it is equipped with two cameras,
gyroscopes and accelerometers over the three axes and iscovered by a high number of touch sensors.
While pneumatic actuators as the ones employed in CB2
permit a safe interaction as well as smooth and natural
looking movements we decided to employ PID controlled
servomotors for the actuation of our compact baby robot.
This allowed us both to ease the control, which can be
performed without problems using an internal ARM based
motor control board, and to remove the necessity of connecting the robot to a compressor or compressed air tank. We
based our platform on VisiON 4G, a commercial productmanufactured by VStone. The high performances of thisrobot and its ability to perform whole body dynamic motions
could be verified in the Robocup [6] competition, won

Neck
yaw
Neck
pitch
Right shoulder
pitch
Right shoulder
roll
Right elbow
rollFig. 1. Photo of the developed baby robot (left), head covered with urethane
foam (right top) and detail leg parallel link mechanism (right bottom).

Fig. 2.

Left shoulder
pitch

Right hipyaw

Left hipyaw

Right hippitch

Left hippitch

Right hiprollLeft hiprollRight knee
pitch 1

Left knee
pitch 1

Right knee
pitch 2

Left knee
pitch 2Right ankle
rollLeft ankle
rollRight ankle
pitch

Left ankle
pitch

Left shoulder
roll
Left elbow
roll

Degrees of freedom of the developed baby robot.
TABLE Iby Team Osaka using VisioON 4G for two consecutive
years. The robot, developed to resist to the impacts with
opponents during the Robocup games, reveals to be very
robust. The touch sensors covering the robot surface of ourresearch platform were designed aiming at maintaining thisrobustness, and in fact the presence of elastic elements intheir structure even improves the robot absorption of shocks.
We plan to make the sensor option commercially available
in a near future. We in fact expect our research platform
to be an interesting trade off between cost and richness ofavailable sensory information.
Section II illustrates the robot features and in particularsubsection II-B provides a description of the touch sensor
available on our platform. In section III we describe two
research themes that will be pursued using the presented
platform. We conclude in section IV by summarizing thecontent of the paper.
II. ROBOT D ESCRIPTION
A. Servomotors and control boards
As stated in the introduction the developed researchplatform is based on the robot employed by Team Osaka,
VisiON 4G, which is a commercial product manufactured
by VStone1 . The robot has 22 degrees of freedom (DOFs)
powered by metal gear PID controlled servomotors. Employing servomotors allows an easy position control, without thestrong nonlinearity problems presented by artificial muscle as
the pneumatic actuators used in CB2 . More precisely VisiON
4G presents 7 DOFs for each leg, 3 for each arm and 2 DOFs,
expressly pan and tilt, for the head, as shown in Fig. 2. The
legs present a parallel link structure, as visible in Fig. 1, toincrease the leg robustness. As can be seen in the photos the

VS-SV410 CHARACTERISTICS
size
weighttorquespeed
range
power supply

40.5 × 21.0 × 32.9mm
62g
41Kgf cm (at 16.8V)
0.14s/60◦
180◦
min 10V max 18V

robot is completely realized in aluminum, ensuring robustness over other low cost hobbyist robots that include plastic
parts as, for instance, the very diffused Kondo HRV series2 .
The servomotors, unlike conventional PWM servomotors,present a serial input that allows chaining them over a serial
bus, strongly reducing the required wiring. Table I provides adetailed specification of their characteristics. Having a serial
bus allows a rich protocol that permits to runtime set target
positions as well as the PID parameters like the gain, and toread the temperature, position and voltage. The servomotorand in particular their high gear ratio allows very fast andwhole-body dynamic motions, as visible in figures 3 and 4.
The robot is powered by two Nickel-metal hydride battery
packs lodged in the robot’s torso that provide 14.8V and
1400mAh. The main body also contains a CPU board, amicrocontroller based motor control board, a board with two
gyroscopes and three accelerometers (VStone VS-IX001)
and two speakers. Two CMOS USB cameras and two microphones installed in the head, allows for binocular visionand detection of external sounds despite the very compact
dimensions of the robot. The CPU board consists of a Pinon3
PNM-SG3 500MHz that thanks to its x86 compatible Geode
LX800/CS5536 processor and its 512Mb of DDR400 RAM
2 http://www.kondo-robot.com/

1 http://www.vstone.co.jp

3 http://www.pinon-pc.co.jp/

Fig. 3.

0.0 sec

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Crawling motion executed by the developed platform.

allows running conventional operating systems like Windows
XP or Linux on the robot while keeping the power consumption very low (only 5W). As a conventional computera VGA output (resolution 1920x1440, 32bit color depth), a
USB bus and two serials are provided. Storage is provided
by a compact flash disk, and a supplementary compact flash
slot allows plugging in a network card for connection toconventional IEEE 802.11b and 802.11g wireless LANs. In
order to allow real time motor control without using CPU
resources the robot includes a 60MHz ARM7 microcontroller
based motor board (VStone VS-RC003) that can communicate to the main CPU board through a serial connection.
The motor control board presents also a USB mini B
connector for direct communication with an external PC.
The USB communication protocol is managed by VStone
SDK that allows development of motions and acquisition ofgyroscope and accelerometer information by a set of highlevel API. These API are fully integrated with the Robovie
Maker development environment released by VStone andit is possible, for instance, to create robot motions by aFig. 4.

Turn over motion executed by the developed platform.

classical slider based interface and to execute it by few lines
of code. Simple signals can be sent to the ARM board also by
a wireless bluetooth Playstation 3 controller (SCPH-98040,
trademarked ”SIXAXIS”), permitting, for instance, real time
execution of different sets of motions.
B. Touch sensors
As reported in the previous section the developed platform
is an extension of a commercial product called VisiON 4G.
However the presence of a high number of touch sensors
despite its reduced size distinguishes it from its commercial
counterpart and in general from small humanoid robots
available on the market. Figure 7 indicates the location of
the 90 pressure sensors placed over the whole robot.
The structure and working principle of each of the touch
sensors is schematized in Figure 6. A small (12x4 mm) board
equipped photo interrupter is attached to robot aluminum
frame (see Fig. 5). A plastic plate, visible in figure 5, isfixed to the frame by a screw tightened over a spacer and isusually kept at a fixed distance from the frame by an elastic
material that functions as a spring. A white disc attached to

84

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Fig. 5.
Photo of the photo interrupter board attached to the robot’s
aluminum frame and plastic plate

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Plate
Spring element

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Robot body
Photo interrupter
Fig. 6.

Schema of our touch sensor.
Fig. 7.

the plate reflects the light coming from the photo emitter tothe phototransistor of the photo interrupter. The amount ofreflected light changes depending on the distance between
the photo interrupter and the white disc, i.e. depending on
the pressure applied to the plate, allowing detection of forces
applied to the plate.
Although the relationship between the applied force andthe reflected light measured by the phototransistor is strictly
monotonically increasing, it is non-linear and the small
differences in the elasticity of the materials make calibrationnecessary for each of the sensors. In particular, the range ofthe values read by each of the photointerrupters is different.
In human-robot interaction studies, where categorizing thetouch in two or three classes (no pressure, soft pressure,strong pressure) often suffices a simple calibration that
determines the offset value and the range of each sensor canbe sufficient.
Groups of up to 8 photodiodes are connected to 19x35mm
boards that manage the A/D conversion of the signals.
Nineteen of such boards, placed over the whole robot, arechained by a serial bus that can be used to read the data
coming from all sensors with a simple polling protocol
similar to the servomotor one.
III. A PPLICATIONSIn this section we introduce two of the research topics thatwill employ the baby platform presented in the previous sections. Expressly subsection III-A will describe a biologicallyinspired approach able to learn parameters suitable to makea robot crawl and subsection III-B will briefly illustrate theidea of employing touch to teach motions to a humanoid
robot.
A. Biological fluctuations for crawling learning
Often simple living beings like bacteria present a highly
adaptive and robust behavior despite their structural simplicity. For instance bacteria are able to sense changes in
the concentration of nutrients and direct their movements

Arrangement of the touch sensors on the robot body.

toward the food molecules while escaping from poisoning
substances without any complex planning strategy.
Among the various alternatives for the microorganism
movement toward or away from the chemical stimuli, a
process usually termed as chemotaxis, the behavior of Escherichia Coli (in the following referred as E. Coli) has
been deeply studied [7]. These organisms utilize a biased
random walk for their movement. In particular, these bacteria
have only two way of moving, rotating clockwise or counterclockwise. When they rotate counter-clockwise the rotationaligns their flagella into a single rotating bundle and theyswim in a straight line. Conversely clockwise rotations breakthe flagella bundle apart and the bacteria tumble in place.
The bacteria cannot therefore choose the direction of their
movement, but just keep alternating clockwise and counterclockwise rotations. In absence of chemical gradients thelength of the straight line paths (counter-clockwise rotations)is independent of the direction, and the bacteria essentiallyperform a random walk. In case of an increasing gradient of
attractants (like food) the bacteria instead reduce the numberof tumbles, i.e. proceed in the same direction for a longer
time and the overall movement is directed toward increasing
concentrations of the attractant. This kind of behavior, which
is more and more deterministic the better the conditions are
and conversely more and more stochastic the worse the stateis can be formalized under the very general framework of
biological fluctuations [8], [9]. Expressly assuming to havea continuous time system the model of biological fluctuations
is given by the equationẋ = Af (x) + η.

(1)

where x ∈ Rm is the control signal or represents thevalue of some parameter that determine the behavior (of theanimal, or, in our case, of the robot), f : Rm → Rm is adeterministic function of the current value of x, η is a randomvariable and A : Rn → R is a function, called “activity”, thatindicates the fitness, or “quality” of a particular state of theliving being/robot. Intuitively when the state is getting betterthe value of A increases and the control actions becomesmainly deterministic, while when the conditions worsen thecontrol becomes more and more stochastic. This mechanism
can simply search high-fitness states while utilizing internaland external constraints. We are investigating a possibility
that this mechanism underlies human motion generation,such as motion emergence from motor babbling in infantdevelopment.
In [10] we showed by simulation results that a crawling
motion can be achieved by a robot using this simple technique. Expressly we simply controlled each robot joint bya sinusoidal function and set x as the space of the phasesof these sinusoids. We then defined f (x) as the negative
gradient of a surface given by the sum of 20 randomly placed
Gaussian shaped holes, and A as a function of the velocity inthe forward direction (i.e. in the direction defined by vector
going from the robot’s torso to its head center).
B. Teaching by Touching
Tactile interaction is a fundamental aspect of interaction
between babies and their mothers [11]. We aim at studying
the mechanism of the development of intention interpretation
through what we termed “Teaching by touching” in [12].
Briefly, touch is an intuitive method of communication
employed in human-human as well as in human-machineinteraction [13]. For instance touch is frequently used by
sports coaches or dance instructors [14] to correct a learner’s
posture or motion. Tactile interaction therefore appears particularly appealing as an intuitive method for humans to teachrobots. Our goal is to validate the idea of employing touchas a way for inexperienced users to program robot motions
intuitively.
The systems available on the market requires the users tothink at the motion in terms of keyframes and for each of thiskey-postures the angles of each robot joint must be specified
by a moving slider. This process is not straightforward, since,
for instance, the user must identify which is the joint thatmoves the robot parts in the desired direction. Conversely, we
can imagine that for novice users directly touching the robot
parts whose movement should be modified would be quite
intuitive, as in general with kinesthetic demonstration [15].
Essentially the teachers’ touching is a method of transmitting
their internal image of what the robot postures should be.
To make communication successful, the robot must theninterpret these touches in terms of adjusted body postures.
However, for the robot this reconstruction process is notstraightforward since similar touches could have different
meanings depending on the context. For example if the robot
is standing, touching the upper part of one leg could meanthat the leg should bend further backwards. However if therobot is squatting, the same touch could mean that the robot
should move lower to the ground by bending its knees (seeFig. 8).
Furthermore, the style and method of touching could bein part or totally user-dependent. Defining a fixed protocol
and forcing the user to employ it would allow solving theseFig. 8. An example of the context dependence of the touch meaning.
The user presses the same sensor, but due to the different robot posture thedesired posture modifications (bend the leg and bend the knees, respectively)
differs.

ambiguity problems. However, we believe that by making
the robot’s instruction interpretation adaptive users will beable to touch the robot more naturally and therefore develop
motions with a very low mental effort. Figure 9 summarizes graphically our model for the emergence of touch
instructions. According to our model users have an intended
joint modification and, depending on some of the featuresof the physical context that they unconsciously perceive,
they provide a tactile instruction. In [12], we presented aninterface that uses the K-Nearest Neighbor algorithm witha specifically designed weighting to construct the inverse
mapping from touch instruction and physical context tointended joint modification.
Our system allows the users to switch between twomodalities. In the “motion-development” phase the users
press touch sensors and the robots moves according to its
interpretation of the touch pattern. When users feel that the
robot interpretation does not reflect their intention, they justswitch to the “touch-meaning-teaching” phase and provide,by other means (direct robot manipulation or classical, slider
based interface) the posture modification they intended by
the applied touch. In this way the (supervised learning)algorithm for touch interpretation can be refined more andmore online, i.e. during motion development. This approachpresents an interesting secondary advantage: the collectedtraining examples, consisting of a touch pattern and thecorresponding desired joint modification, can be studied
afterwards to help improve our understanding of how humans
communicate via touch.
Preliminary results were obtained using simulated touch
sensors. Expressly we used a simulator, depicted in Fig. 10,
and asked the users to develop motions by clicking on thesimulated touch sensors. The experiments show that simple
linear models failed to capture the structure of the mapping
between touch instructions and desired joint modification.
Conversely, our algorithm revealed to perform well (at leastin comparison to ridge regression) on unseen validation dataand pilot experiments suggest that the employed system
can reduce the motion development time with respect toclassical slider based interfaces. The presence of a stronguser dependency of the touch protocol was identified using
a limited number of users and in detail we got insights on thefact that different users employ different levels of abstractionin their instructions:• a nearly fixed mapping from a small set of sensors tohow learning of a crawling motion can be achieved bya simple, biologically inspired approach. Successively, we
presented an application that will make full use of the touch
sensors available on the robot to develop robot motions
and, more importantly, to study the way humans employ
touch to convey information. Short term future works willclearly involve the actual implementation of the presented
experiments, currently conducted only in simulation, on thereal robot.
Fig. 9.

Conceptual schema of the generation of touch instructions.

V. ACKNOWLEDMENTS
The authors would like to thank VStone for the development of the hardware presented in this paper. The research ofsection III-A was supported by “Special Coodination Funds
for Promoting Science and Technology: Yuragi Project” ofthe Ministry of Education, Culture, Sports, Science andTechnology, Japan.
R EFERENCES

Fig. 10. Screenshot of the simulator used in our experiments. A mouseclick on a simulated touch sensor on the robot arm makes the sensor color
change for clarity.

the joints on which the context has little or no influencea mapping based on physical considerations; in thiscase, the context, for instance the position of the ground,
becomes crucial• a very high level representation of the motion, where for
instance just the limb that should be moved is indicatedby touching; at this level of abstraction a single touchcorresponds to a motion primitive.
The developed platform, equipped with touch sensors on
the whole body, will permit direct interaction with the realrobot, without the need of simulated touch sensors and willallow us to study tactile interaction in a more natural setting.
•

IV. C ONCLUSIONSIn this paper we introduced a newly developed baby
robot. Thanks to its compact size this platform allows a safeand easy interaction while providing most of the sensory
information given by bigger size and more complex childrobots spreading in developmental robotics. We briefly illustrated its onboard components, and in detail the CPU andmotor control board that makes it completely autonomous.
We then described the touch sensors that cover its wholebody and that make it unique in the small humanoid robot
field. Two examples of researches that will be pursued on
the platform had then been presented. Firstly we outlined[1] M. Asada, K. Hosoda, Y. Kuniyoshi, H. Ishiguro, T. Inui,Y. Yoshikawa, M. Ogino, and C. Yoshida, “Cognitive developmental
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M. Asada, “Cb2: A child robot with biomimetic body for cognitive
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[8] I. Fukuyori, Y. M. Yutaka Nakamura, and H. Ishiguro, “Flexible
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[11] T. Field, Touch, M. Press, Ed. Bradford Books, 2003.
[12] F. DallaLibera, T. Minato, I. Fasel, H. Ishiguro, E. Pagello, and
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